CN113367679B

CN113367679B - Target point determination method, device, equipment and storage medium

Info

Publication number: CN113367679B
Application number: CN202110757474.0A
Authority: CN
Inventors: 魏可成; 王也喆; 张维; 张琼
Original assignee: Beijing Yone Galaxy Technology Co ltd; Beijing Yinhe Fangyuan Technology Co ltd
Current assignee: Beijing Yinhe Fangyuan Technology Co ltd
Priority date: 2021-07-05
Filing date: 2021-07-05
Publication date: 2023-04-18
Anticipated expiration: 2041-07-05
Also published as: WO2023280086A1; EP4342373A1; CN113367679A

Abstract

The present disclosure provides a target determination method, a target determination apparatus, an electronic device, a storage medium, and a neural modulation apparatus, wherein the target determination method includes: acquiring scan data of a subject, wherein the scan data comprises data resulting from magnetic resonance imaging of the brain of the subject; determining at least two regions of interest of the subject based on the scan data; determining at least one abnormal region of interest in the at least two regions of interest according to a preset abnormal detection rule; determining a target point based on the at least one abnormal region of interest. Realizes the positioning of the individual nerve regulation target point of the testee.

Description

Target point determination method, device, equipment and storage medium

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to a target determination method and apparatus, an electronic device, a storage medium, and a neural modulation device.

Background

Many neurological and psychiatric diseases are often not clearly focalized and only manifest as neurological dysfunction. The adjustment of abnormal function network directly or indirectly by utilizing nerve regulation means such as electricity, magnetism, light, ultrasound and the like is an important means for improving the symptoms of patients. How to select a neuromodulation target in the human brain is a difficult problem. Research has shown that for most of the neurological and psychiatric diseases, ideal regulation and treatment effects cannot be usually obtained due to the failure to directly analyze the cause from the structural image and to locate the focus. Therefore, there is a clinical need for an objective, accurate and quantifiable aid to assist physicians in screening for individualized neuromodulation targets. Existing methods for determining neuromodulation targets do not meet this need.

Disclosure of Invention

The disclosure provides a target determination method, a target determination device, an electronic device and a storage medium, which are used for screening individual nerve regulation targets.

In a first aspect, the present disclosure provides a target determination method, including: acquiring scan data of a subject, wherein the scan data comprises data resulting from magnetic resonance imaging of the brain of the subject; determining at least two regions of interest (ROIs) of the subject based on the scan data; determining at least one abnormal ROI in the at least two ROIs according to a preset abnormal detection rule; determining a target point based on the at least one aberrant ROI.

In some alternative embodiments, said determining at least two ROIs of the subject based on the scan data comprises:

determining the connectivity between every two voxels in the scanning data to form a brain connection matrix corresponding to the scanning data;

forming the at least two ROIs based on a brain region template of a standard brain and the brain connection matrix.

In some alternative embodiments, said determining at least two ROIs of the subject from the scan data comprises:

determining a connectivity between every two voxels in the scan data;

dividing the scan data into a plurality of large regions corresponding to the brain anatomy of the subject, dividing the plurality of large regions into a plurality of brain regions, wherein each brain region of the plurality of brain regions comprises at least one voxel;

and fusing brain areas with the voxel connectivity higher than a preset brain area voxel connectivity threshold value among the brain areas to form the at least two ROIs.

determining at least two ROIs of the subject from the scan data based on a volume standard brain structure template.

determining at least two ROIs of the subject from the scan data based on a cortical standard brain structure template.

In some optional embodiments, the determining at least one abnormal ROI among the at least two ROIs according to a preset abnormal detection rule includes:

acquiring group brain magnetic resonance data;

determining a population brain connection matrix according to the population brain magnetic resonance data;

determining the at least one abnormal ROI from the population brain connectivity matrix and the brain connectivity matrix.

In some alternative embodiments, the determining a target point based on the at least one abnormal ROI comprises:

determining whether the at least one abnormal ROI is located in a regulatable brain region;

if so, determining the center of the at least one abnormal ROI as the target point, or determining a region which takes the center of the at least one abnormal ROI as a sphere center and a preset target point radius as a first target point ROI, and determining the target point according to the position of the first target point ROI;

if not, determining the connection degree of the at least one abnormal ROI and other ROIs in the at least two ROIs, and determining the ROIs which are positioned in the adjustable and controllable area and have the connection degree with the at least one abnormal ROI exceeding a preset connection degree threshold value in the other ROIs as second target point candidate areas;

and determining the center of the second target point candidate region as the target point, or determining a region with the center of the second target point candidate region as a sphere center and a preset target point radius as a second target point ROI, and determining the target point according to the position of the second target point ROI.

determining the brain structure partition where the target is located according to the disease type of the subject;

determining the intersection of the at least one abnormal ROI or the ROI with the abnormal ROI connectivity meeting a preset connectivity threshold condition and the brain structure partition as a target candidate area;

and determining the center of the target spot candidate region as the target spot, or determining a region with the center of the target spot candidate region as a sphere center and a preset target spot radius as a target spot ROI, and determining the target spot according to the position of the target spot ROI.

In some alternative embodiments, the functional magnetic resonance imaging comprises: structural magnetic resonance imaging, and/or task state functional magnetic resonance imaging, and/or rest state functional magnetic resonance imaging.

In a second aspect, the present disclosure provides a target determination device, comprising: a data acquisition unit configured to acquire scan data of a subject, wherein the scan data comprises data resulting from magnetic resonance imaging of the brain of the subject; a processing unit configured to determine at least two ROI of interest of the subject based on the scan data; an abnormality detection unit configured to determine at least one abnormal ROI among the at least two ROIs according to a preset abnormality detection rule; a target determination unit configured to determine a target based on the at least one abnormal ROI.

In some optional embodiments, the processing unit is further configured to:

determining a connectivity between every two voxels in the scan data;

and fusing brain areas of which the voxel connection degree between the brain areas is higher than a preset brain area voxel connection degree threshold value in the plurality of brain areas to form the at least two ROIs.

In some optional embodiments, the processing unit is further configured to:

In some optional embodiments, the anomaly detection unit is further configured to:

acquiring group brain magnetic resonance data;

determining a group brain connection matrix according to the group brain magnetic resonance data;

determining the connectivity between every two voxels in the scanning data to form a brain connection matrix of the subject corresponding to the scanning data;

determining the at least one abnormal ROI from the population brain connectivity matrix and the subject brain connectivity matrix.

determining the intersection of the at least one abnormal ROI or the ROI with the abnormal ROI connectivity meeting a preset connectivity threshold condition and the brain structure partition as a target candidate region;

In some alternative embodiments, the magnetic resonance imaging comprises: structural magnetic resonance imaging, and/or task state functional magnetic resonance imaging, and/or rest state functional magnetic resonance imaging.

In a third aspect, the present disclosure provides an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by the one or more processors, cause the one or more processors to implement the method as described in any implementation of the first aspect.

In a fourth aspect, the present disclosure provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by one or more processors, implements the method as described in any of the implementations of the first aspect.

In a fifth aspect, the present disclosure provides a neuromodulation device configured for neuromodulating a target of a subject in accordance with a preset neuromodulation protocol; wherein the target is determined according to a method as described in any one of the implementations of the first aspect.

In some alternative embodiments, the predetermined regulatory regimen comprises at least one of:

deep brain electrical stimulation;

transcranial electrical stimulation;

electrical tic therapy;

electrical stimulation based on cortical brain electrodes;

transcranial magnetic stimulation;

regulating and controlling an ultrasonic focusing nerve;

magnetic resonance guide high-energy ultrasonic focusing treatment regulation and control;

and (4) regulating and controlling light stimulation.

In order to achieve the determination of the neuromodulation target, the currently commonly used technical means include:

1. determining a neuromodulation target based on a group level task state fMRI (functional magnetic resonance imaging); the method has the following defects: the task state fMRI has low signal-to-noise ratio and low repeatability, and requires a subject to have certain cognitive level; the functional region result of the task-state fMRI is greatly influenced by task design; determining the baseline level of a functional zone is difficult.

2. With clinical experience based on brain anatomy, the neuromodulation target is determined by finding the approximate location of a surface projection of a specific functional zone on the surface of the patient's scalp, such as the left dorsolateral Prefrontal Cortex (DLPFC) localization method (often referred to as the "5cm" localization method) that is approved by the united states Food And Drug Administration (FDA) for repeated transcranial magnetic stimulation (rTMS) to treat treatment-resistant depression; the method has the following defects: the individual anatomical structure difference is ignored, and the positioning precision is low, so that the positioning of the nerve regulation target point is not accurate; ignoring individual functional network differences, the target location may be located in other brain functional areas.

3. Determining a nerve regulation target point according to the electrode cap, such as an international 10-20 electrode cap positioning method; the method has the following defects: the individual anatomical structure difference is ignored, and the positioning precision is low, so that the positioning of the nerve regulation target point is not accurate; individual functional network differences are ignored.

4. Determining a neuromodulation target based on an anatomical structure or a population-averaged fMRI study-defined ROI; the method has the following defects: various nerve and mental diseases are not clear to cause focus, only show abnormal functions of a nervous system, and cannot reflect the characteristics of the diseases by a simple anatomical structure; the etiology of neurological and psychiatric diseases is complex, and in addition to individual differences, treatment regimens based on population-averaged fMRI are inefficient.

5. Determining a nerve regulation target point according to the tissue structure metabolism condition reflected by the PET scanning data; the method has the following defects: PET scanning is expensive, which increases the medical burden; scanning certain radiation; PET scanning is limited in the number of neurological and psychiatric disorders to which it is applicable; the image signal to noise ratio is low, the accuracy of determining the target point is influenced by unclear boundaries of anatomical structures, and the clinical treatment effectiveness is low.

The present disclosure provides a target determination method, apparatus, electronic device and storage medium, by acquiring scan data of a subject, wherein the scan data includes data obtained by performing magnetic resonance imaging on a brain of the subject, determining at least two ROI of interest of the subject based on the scan data; determining at least one abnormal ROI in the at least two ROIs according to a preset abnormal detection rule; determining a target point based on the at least one aberrant ROI. The functional magnetic resonance imaging is utilized to depict brain scanning data of the subject, the brain area of the subject is determined, on the basis of fully considering individual differences, the problem that the neural regulation target point is inaccurate due to the fact that individual structure or function differences are not considered in the traditional method can be effectively solved, and the positioning of the individual neural regulation target point of the subject is realized. The individual abnormal detection method combines the brain structure information and the function information, can efficiently detect the brain area with abnormal brain connection mode compared with normal people, and can effectively solve the problem that the nerve regulation target point is inaccurate because individual structure or function difference is not considered in the traditional method.

Drawings

The drawings illustrate generally, by way of example, but not by way of limitation, various embodiments discussed herein.

FIG. 1 is an exemplary system architecture diagram in which one embodiment of the present disclosure may be applied;

FIG. 2 is a schematic flow chart diagram of one embodiment of a target determination method according to the present disclosure;

FIG. 3 is an exploded view of one embodiment of step 202 of the target determination method shown in FIG. 2;

FIG. 4 is an exploded view of yet another embodiment of step 202 of the target determination method shown in FIG. 2;

FIG. 5 is a schematic block diagram of one embodiment of a target determination device according to the present disclosure;

fig. 6 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of the present disclosure.

Detailed Description

So that the manner in which the features and elements of the disclosed embodiments can be understood in detail, a more particular description of the disclosed embodiments, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings.

In the description of the embodiments of the present disclosure, it should be noted that, unless otherwise specified and limited, the term "connected" should be interpreted broadly, for example, it may be electrically connected, it may be a communication between two elements, it may be directly connected, or it may be indirectly connected through an intermediate medium, and a person skilled in the art may understand the specific meaning of the above terms according to specific situations.

It should be noted that the terms "first \ second \ third" related to the embodiments of the present disclosure are only used for distinguishing similar objects, and do not represent a specific ordering for the objects, and it should be understood that "first \ second \ third" may interchange a specific order or sequence when allowed. It should be understood that "first \ second \ third" distinct objects may be interchanged under appropriate circumstances such that the embodiments of the present disclosure described herein may be practiced in an order other than those illustrated or described herein.

FIG. 1 illustrates an exemplary system architecture 100 to which embodiments of the target determination methods or target determination apparatuses of the present disclosure may be applied.

As shown in fig. 1, the system architecture 100 may include

terminal devices

101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. Various communication client applications, such as a magnetic resonance imaging control application, a functional magnetic resonance imaging control application, a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like, may be installed on the

terminal devices

101, 102, 103.

The

terminal apparatuses

101, 102, and 103 may be hardware or software. When the

terminal devices

101, 102, 103 are hardware, they may be various electronic devices with display screens, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like. When the

terminal devices

101, 102, 103 are software, they can be installed in the above-listed devices of the plurality of brain regions of the electronically determined subject. It may be implemented as a plurality of software or software modules (e.g., processes for providing a brain atlas) or as a single software or software module. And is not particularly limited herein.

The server 105 may be a server that provides various services, such as a background data processing server that processes scan data transmitted by the

terminal devices

101, 102, 103. The background data processing server can determine a plurality of brain areas of the subject and voxels corresponding to each brain area to feed back to the terminal equipment according to the scan data.

The server 105 may be hardware or software. When the server 105 is hardware, it may be implemented as a distributed server cluster composed of a plurality of servers, or may be implemented as a single server. When the server 105 is software, it may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services), or as a single piece of software or software module. And is not particularly limited herein.

It should be noted that the targeting method provided by the present disclosure is generally performed by the server 105, and accordingly, the targeting device is generally disposed in the server 105.

It should be noted that, in some cases, the target point determination method provided by the present disclosure may be executed by the server 105, the

terminal devices

101, 102, and 103, or by both the server 105 and the

terminal devices

101, 102, and 103. Accordingly, the target point determination device may be disposed in the server 105, or disposed in the

terminal devices

101, 102, and 103, or partially disposed in the server 105 and partially disposed in the

terminal devices

101, 102, and 103. And accordingly system architecture 100 may include only server 105, or only

terminal devices

101, 102, 103, or may include

terminal devices

101, 102, 103, network 104 and server 105. The present disclosure is not limited thereto.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to FIG. 2, a flow 200 of one embodiment of a target point determination method according to the present disclosure is shown. The target point determination method comprises the following steps:

at step 201, scan data of a subject is acquired.

In an embodiment of the disclosure, the scan data includes data obtained by magnetic resonance imaging of the brain of the subject.

The scan data includes a BOLD signal sequence corresponding to each voxel of a preset number of voxels.

In this embodiment, an executing entity (e.g., the server shown in fig. 1) of the target point determination method may first obtain scan data of the subject from other electronic devices (e.g., the terminal device shown in fig. 1) connected to the executing entity through a network, locally or remotely.

Voxels are also called voxels, which are short for volumetric pixels. A voxel is conceptually similar to the smallest unit of a two-dimensional space, a pixel, which is used on the image data of a two-dimensional computer image. The voxel is the minimum unit of digital data on three-dimensional space segmentation, and is applied to the fields of three-dimensional imaging, scientific data, medical images and the like.

The BOLD signal sequence corresponding to the voxel means that the subject is scanned by magnetic resonance, a BOLD signal is obtained for each voxel at intervals of a preset time unit, a period of BOLD signals are finally obtained, the BOLD signals are arranged according to the sequence of the acquisition time, and the BOLD signal sequence corresponding to each voxel is obtained, wherein the number of the contained BOLD signals can be an integer quotient obtained by dividing the time length corresponding to the target task by the preset time unit. For example, the scanning time duration is 300 seconds, and the preset time unit is 2 seconds, then 150 BOLD values in the BOLD signal sequence corresponding to each voxel may also be considered as 150 frames of data in the BOLD signal sequence corresponding to each voxel, or may also be considered as a vector with a dimension of 150 dimensions, or may also be considered as a matrix with a1 × 150 order in the BOLD signal sequence corresponding to each voxel, which is not specifically limited by the present disclosure.

It is understood that the specific number of voxels included in the scan data may be determined according to the scan accuracy of functional magnetic resonance imaging or magnetic resonance imaging, or may be determined according to the accuracy of an imaging device, where the preset number is not limited to the specific number of voxels, and in the current practical application, the number of voxels included in the human brain scan data is measured in tens of thousands or hundreds of thousands, and as the scanning technology advances, the number of voxels included in the human brain scan data can be further increased.

In the present disclosure, the execution main body may acquire scan data of the subject from other electronic devices (for example, a terminal device shown in fig. 1) connected to the execution main body through a network, locally or remotely.

In an embodiment of the present disclosure, magnetic resonance imaging may include: structural magnetic resonance imaging, and/or task state functional magnetic resonance imaging, and/or rest state functional magnetic resonance imaging.

Data obtained by functional magnetic resonance imaging contain time series information, and correspond to a four-dimensional image. For example: acquiring a functional magnetic resonance imaging image, acquiring a 3-dimensional image matrix (Length x Width x Height, lxM x N), acquiring 150 frames of data every 2 seconds, and acquiring 150 frames of data in 6 minutes to form a functional magnetic resonance imaging data signal of LxMxN voxels x 150.

Data obtained by structural magnetic resonance imaging is a high-resolution three-dimensional gray scale anatomical structure image, such as T1w (T1 weighted imaging — tissue-highlighted T1 relaxation (longitudinal relaxation) differences) and related images thereof, T2w (T2 weighted imaging — tissue-highlighted T2 relaxation (transverse relaxation) differences) and related images thereof, a liquid attenuated inversion recovery sequence (FLAIR) and related images thereof; structural magnetic resonance imaging may also include magnetic resonance diffusion imaging, such as: diffusion-weighted imaging (DWI) and related images thereof, diffusion Tensor Imaging (DTI) and related images thereof, and the like.

DTI is a magnetic resonance technique used to study central nervous system anatomic fascicle dispersion anisotropy and to visualize white matter fiber anatomy, probing tissue microstructures through the anisotropy of water molecule dispersion in tissue (anisotropy). The anisotropy of white brain matter is caused by parallel-running myelin axonal fibers, and the diffuse of white brain matter is the largest in the direction of parallel nerve fibers, i.e., the fractional diffuse anisotropy (FA) is the largest, and can be approximately determined to be 1 (actually, a fraction greater than 0.9 and approaching 1). This property is marked with a color to reflect the spatial directionality of the white matter, i.e., the direction of fastest diffusion indicates the direction of fiber travel. Imaging of the fiber bundle by DTI yields a brain connection matrix that reflects brain structure.

In an embodiment of the present disclosure, functional magnetic resonance imaging may include: task state functional magnetic resonance imaging, and/or rest state functional magnetic resonance imaging.

It is to be understood that resting state functional magnetic resonance imaging is magnetic resonance imaging resulting from a magnetic resonance scan of the brain of a subject while the subject is not performing any tasks during the scan. Correspondingly, the task-state functional magnetic resonance imaging is magnetic resonance imaging obtained by performing a magnetic resonance scan on the brain of the subject while the subject performs the target task.

After acquiring the brain structure magnetic resonance scan data of the subject, various implementations can be used to determine the brain structure map of the subject from the brain structure magnetic resonance scan data of the subject, i.e., to obtain what structural components are in specific regions of the brain of the subject. For example, it can be implemented using existing software for processing three-dimensional brain scan data, such as free cortical reconstruction (freescan) magnetic resonance data processing software. For example, the deep learning model may be trained in advance based on a large amount of brain structure image scan sample data and the label of the corresponding brain structure component, and then the brain structure magnetic resonance scan data of the subject may be input into the deep learning model obtained by training to obtain the corresponding brain structure map.

In some optional embodiments, the executing body performs preprocessing on the scan data after acquiring the scan data of the subject.

In the present disclosure, the processing method of the pretreatment is not particularly limited, and the pretreatment may include, for example:

functional magnetic resonance imaging image preprocessing, for example,

(1) Temporal layer correction, panning correction, temporal signal filtering, noise component regression, spatial smoothing, and the like.

(2) The functional magnetic resonance imaging image is registered with the structural image (if any).

(3) The functional magnetic resonance imaging signals are projected onto structural images (if any), including reconstructed images of the individual cortex or related groups of average level structural images.

The structural magnetic resonance imaging image is preprocessed (if the structural image exists), such as skull removal, field intensity correction, individual anatomical structure segmentation, cerebral cortex reconstruction and the like.

At least two ROIs of the subject are determined based on the scan data, step 202.

The present disclosure provides a number of alternative implementations for step 202 described above.

Fig. 3 is a partially exploded schematic view of an example of step 202 in the target point determination method shown in fig. 2, and in some alternative embodiments, as shown in fig. 3, the step 202 may specifically include:

step 202a1, determining the connectivity between every two voxels in the scan data to form a brain connection matrix corresponding to the scan data.

In the present disclosure, the connection degree of the voxel and the ROI may include an average value of the connection degree of the voxel and each voxel in the ROI; the connectivity between the two ROIs, which may include the average of the connectivity of the voxels in the two ROIs with each voxel in the other ROI; the connection degree of the voxel and the brain area can comprise an average value of the connection degree of the voxel and each voxel in the brain area; the connectivity between two brain regions may comprise an average of the connectivity of voxels in each of the two brain regions with voxels in the other brain region.

The degree of connectivity characterizes the degree of connectivity of brain connections, and can also be expressed as a degree of correlation. Here, brain connections include functional connections and structural connections. The functional connection can be obtained by calculating a Pearson correlation coefficient based on a BOLD time sequence corresponding to the pheromone in the ROI; structural connections include structural connections between ROIs as obtained from fiber bundle imaging, and the like.

For example, assuming that the number of voxels in the scan data is 10 ten thousand, the BOLD signal sequence corresponding to each voxel includes T BOLD values, and T is the sampling number of the time dimension corresponding to the scan time, the brain connection matrix corresponding to the scan data is a 10 ten thousand × 10 ten thousand order matrix, and the brain connection matrix can represent the degree of connection between every two voxels in the scan data. And calculating the connectivity between the two voxels based on the T BOLD values corresponding to the voxels by using the Pearson correlation coefficient.

In the present disclosure, the correlation coefficient is a pearson (pearson) correlation coefficient, which is a coefficient used to measure the degree of linearity between variables. The calculation formula is as follows:

the formula is defined as: pearson correlation coefficient (p) of two continuous variables (X, Y) _x,y ) Equal to the product (σ) of the covariance cov (X, Y) between them divided by their respective standard deviations _X ,σ _Y ). Coefficients always take on values between-1.0 and 1.0, variables equal or approximately equal to 0 are said to be uncorrelated, and variables equal or approximately equal to 1 or-1 are said to have strong correlation. Here, approximately equal may be understood as a difference from a target value within an error allowable range, for example, in the present disclosure, 0.01 may be approximately equal to 0, or 0.99 may be approximately equal to 1, which is merely an example, and an error allowable range that is approximately equal may be determined according to an accuracy required for calculation in practical applications.

Step 202a2, at least two ROIs are formed based on the brain region template and the brain connection matrix of the standard brain.

Illustratively, a brain atlas comprising more than 2 brain regions may be established for a subject based on a brain region template of a standard brain using pattern recognition or machine learning methods. Methods may include, but are not limited to: independent Component Analysis (ICA), principal Component Analysis (PCA), various types of clustering methods, factor Analysis (factor Analysis), linear Discriminant Analysis (LDA), various matrix decomposition methods, and the like. The resulting brain function network may include different positions of voxels in each functional partition for different subjects, but each voxel is assigned to a specific ROI. I.e., each ROI of the subject, may be a set of voxels made up of voxels in fMRI that have the same function.

In embodiments of the present disclosure, the brain region may include a brain functional partition and/or a brain structural partition.

Fig. 4 is an exploded schematic view of another embodiment of step 202 in the target point determination method shown in fig. 2, and in some alternative embodiments, as shown in fig. 4, the step 202 may specifically include:

step 202b1, the connectivity between every two voxels in the scan data is determined.

Step 202b2, dividing the scan data into a plurality of brain regions corresponding to the anatomical structure of the brain of the subject, and segmenting the plurality of brain regions into a plurality of brain regions, wherein each brain region of the plurality of brain regions comprises at least one voxel.

Step 202b3, brain areas with the voxel connectivity between each brain area higher than a preset brain area voxel connectivity threshold value in the plurality of brain areas are fused to form at least two ROIs.

Illustratively, the brain of a subject is first divided into a plurality of large regions according to major anatomical boundaries; and then, dividing each large area by using functional connection, wherein the connectivity of each voxel is determined according to the reliability (test-test reliability). Dividing each large area to obtain a plurality of brain areas, fusing the brain areas according to the connection degree of voxels contained in the brain areas, combining the brain areas with high correlation of the voxels into one ROI, and finally determining at least two ROIs in the whole brain.

For example, an initial individual brain map can be divided into ten large regions by dividing each of the left and right cortex of the brain into five large regions, the frontal, parietal, occipital, temporal and pancentroidal regions. For another example, the brain may be divided into 4 regions, one for each of the left and right brains, by the high and low cortex of the brain.

In some optional embodiments, the step 202 may specifically include:

a group brain atlas is pre-selected or generated to serve as a brain atlas template, and boundaries corresponding to at least two brain areas in the brain atlas template are projected to the brain scanning data of the subject.

The boundaries of the at least two brain regions are adjusted based on the brain scan data of the subject such that the adjusted brain region boundaries match at least the brain scan data of the subject, forming at least two ROIs.

Illustratively, the group brain atlas is directly projected to the brain of the subject, and then the boundary of the brain region projected by the group brain atlas is gradually adjusted according to the anatomical brain atlas of the subject by adopting a recursive algorithm until the boundary of the brain region tends to be stable. The recursive process will utilize the individual difference distribution of the subject's brain connections, as well as the subject's own brain image signal-to-noise ratio, to determine the magnitude of the boundary adjustment for the brain region. Finally, the brain regions are fused according to the voxel correlation, thereby obtaining at least two ROIs.

In some optional embodiments, the step 202 may specifically include:

and constructing a standard ROI library, wherein the ROI library comprises ROIs related to various test scenes and potential brain connection modes thereof. After the standard ROI library exists, corresponding ROIs can be obtained through screening of test scene types, and then at least two ROIs of the subject are obtained on the basis.

In some optional embodiments, the step 202 may specifically include:

at least two ROIs of the subject are determined from the scan data based on a volume standard brain structure template. Extracting white matter and ventricle areas in the volume standard brain structure template, constructing a volume standard brain structure template binary Mask, removing the white matter and ventricle areas from the Mask to obtain a Mask without the white matter and ventricle areas, and resampling the Mask without the white matter and ventricle areas to obtain at least two ROIs. Or constructing a volume standard brain structure template binary Mask, and performing resampling to obtain at least two ROIs.

In some optional embodiments, the step 202 may specifically include:

at least two ROIs of the subject are determined from the scan data based on a cortical standard brain structure template. Resampling the cortical standard brain structure template generates at least two ROIs. For example, at least two ROIs (e.g., fsaverage6 (fs 6)) of the high-resolution template are generated based on the coarse-resolution cortical surface template (e.g., fsaverage3 (fs 3), fsaverage4 (fs 4)). Specifically, left and right brain vertices of the coarse resolution template are sequentially assigned (1, 2, 3.), resampled (interpolated by a neighbor method) to an fs6 template space, and index numbers of all vertices in an fs6 surface corresponding to the values are sequentially counted according to the assignment sequence, wherein the index numbers are at least two ROIs in the fs6 space, for example: the 13 th vertex in the left brain fs4 surface template is assigned as 13, and 30 vertices with 13 values can be found after resampling to fs6 template space, so that the 13 th ROI of the left brain fs6 surface template consists of the 30 vertices. At least two ROIs are generated based on all the vertexes in the surface template, and for any surface template, each vertex is taken as 1 ROI, for example (the fsaverage6 left brain template comprises 40962 vertexes, and 40962 ROIs can be correspondingly generated).

Step 203, determining at least one abnormal ROI in the at least two ROIs according to a preset abnormal detection rule.

In some optional embodiments, the step 203 may specifically include:

and acquiring group brain magnetic resonance data.

For example, the number of population samples obtained may be above 300. The number of the liquid samples is not specifically limited, but is merely illustrative.

And preprocessing the acquired group brain magnetic resonance data.

Determining a population brain connection matrix from the population brain magnetic resonance data.

Here, the calculation of the population brain connection matrix may include the steps of:

step S11, for each subject' S data of the population magnetic resonance data, calculate the average time series of all vertices/voxels in each ROI. Here, the ROI may be obtained by a method of determining the ROI in the embodiment of the present disclosure, or may be obtained by other existing ROI determining methods.

And step S12, sequentially calculating correlation coefficients (such as Pearson correlation coefficients) between the ROI and the ROI time sequence, and finally obtaining a brain connection matrix of each subject, wherein the brain connection matrix can comprise a functional connection matrix and a structural connection matrix, taking a Functional Connection (FC) matrix as an example, namely ndi _ FC (the matrix is a diagonal matrix, the ith row and jth column number in the matrix represents the correlation between the ith ROI and the jth ROI time sequence, i is less than or equal to N, j is less than or equal to N, N is the number of ROIs, and the size of the ndi _ FC is N x N).

And acquiring a BOLD signal sequence corresponding to each voxel in the scanning data.

Determining the connectivity between every two voxels in the scanning data based on the BOLD signal sequence corresponding to each voxel to form a brain connection matrix of the subject corresponding to the scanning data;

at least one abnormal ROI is determined from the population brain connectivity matrix and the subject brain connectivity matrix.

In some embodiments, step 203 may specifically include scenario one or scenario two. Wherein:

the first scheme is as follows: the anomaly detection algorithm a may specifically include the following steps SA1 to SA3. Wherein:

step SA1, baseline generation (cohort brain connectivity matrix). Specifically, the following sub-steps SA11 to SA14 may be included. Wherein:

substep SA11, data collection: large sample fMRI data (typically, > =300 people) was collected.

Substep SA12, data preprocessing: the data is pre-processed according to the pre-processing method in the embodiment of fig. 2.

Substep SA13 brain connection matrix calculation.

For large sample data per subject data, the average time series of all vertices/voxels in each ROI (generated by step SA1, assuming N ROIs) is calculated.

And sequentially calculating correlation coefficients (such as pearson correlation coefficients) between the ROI and the ROI time sequence, and finally obtaining a brain connection matrix of each sample, wherein the brain connection matrix is not limited to a functional connection matrix and a structural connection matrix, and taking a Functional Connection (FC) matrix as an example, namely, ndi _ FC (the matrix is a diagonal matrix, the ith row and jth column number in the matrix represents the correlation between the ith ROI and the jth ROI time sequence, i is less than or equal to N, j is less than or equal to N, N is the number of ROIs, and the size of the ndi _ FC is N x N).

Substep SA14, baseline generation.

The index _ fc of all subjects were averaged to obtain the mean matrix, i.e., big _ mean _ fc (size N x N).

And sequentially calculating the correlation coefficient of each line of the index _ fc and the corresponding line of the Big _ mean _ fc to obtain a one-dimensional matrix (with the size of N x 1), and finally calculating the average value and the standard deviation of the one-dimensional matrix for all the subjects, namely Corr _ mean (with the size of N x 1) and Corr _ std (with the size of N x 1).

Step SA2, subject FC generation (subject brain connection matrix).

Using the pre-processed data from the subject (set to P), the average time series of all vertices/voxels in each ROI (generated by step 1) was calculated.

Correlation coefficients between the ROI and the ROI time series are sequentially calculated (e.g., using pearson correlation coefficients), and a brain connection matrix of the subject P is finally obtained, where the brain connection matrix may include a functional connection matrix and a structural connection matrix, and is referred to as P _ FC (the size and meaning are the same as ndi _ FC in step SA 1) by taking a Functional Connection (FC) matrix as an example.

The correlation coefficients of each row of p _ fc and the corresponding row of Big _ mean _ fc (generated by step SA 1) are sequentially calculated to obtain an N × 1 one-dimensional matrix, for example: p _ corr.

And step SA3, an anomaly detection algorithm step.

The following operation is carried out on the nth ROI (N is less than or equal to N, and N is the number of ROIs) of p _ corr:

in the formula: p _ Corr, corr _ mean, corr _ std are consistent with the variable names in steps SA1, SA 2.

The matrix p _ z (of size nx 1) is the final anomaly detection result.

Scheme II: the anomaly detection algorithm B may specifically include the following steps SB1 to SB3. Wherein:

step SA1, baseline generation (cohort brain connectivity matrix). Specifically, the following substeps SB11 to SB14 may be included. Wherein:

substep SB11, data collection: large sample fMRI data (typically, > =300 people) was collected.

Substep SB12, data preprocessing: the data is pre-processed according to the pre-processing method in the embodiment of fig. 2.

Substep SB13, brain junction matrix calculation.

Here, the sub-steps SB11 to SB13 have the same contents and effects as the sub-steps SA11 to SA13 in the first step SA1 of the above-mentioned scheme, and are not described again here.

Substep SB14, baseline generation.

The average value of indi _ fc and the standard deviation matrix, i.e. Big _ mean _ fc, big _ std _ fc, of all sample data are calculated.

In step S2, subject FC generation (subject brain connection matrix). Specifically, the following substeps SB21 to SB22 may be included. Wherein:

sub-step SB21, using the pre-processed data of the subject (e.g. P), calculates the average time series of all vertices/voxels in each ROI (generated by step 1).

The sub-step SB22, which calculates the correlation coefficient between the ROI and the ROI time series in turn (for example, using pearson correlation coefficient), finally obtains the brain connection matrix (FC) of each subject, that is: p _ fc (size N x N).

And step SB3, an anomaly detection algorithm step. The method specifically comprises the following steps:

the following operations are carried out on the ith row and jth column (i is less than or equal to N, j is less than or equal to N, and N is the number of ROIs) data in the p _ fc matrix:

wherein: p _ fc, big _ mean _ fc, big _ std _ fc are consistent with the meaning of the variable name in steps SB1, SB 2.

Summing up the values of p _ z (with the size of N x 1) which are larger than the threshold value k (k is an integer which is larger than or equal to 2) in each row to finally obtain an N x1 one-dimensional matrix, namely p _ z _ sum (with the size of N x 1), namely the final abnormal detection result.

Here, the abnormality detection result is that m (m > = 0) brain regions where the subject is abnormal as compared with the normal person, that is, m ROIs are located.

In step 203, the at least one abnormal ROI may be a brain region abnormal ROI, or a cerebellum region abnormal ROI.

Step 204, determining a target point based on the at least one abnormal ROI.

In some optional embodiments, the step 204 may specifically include:

in some alternative embodiments, determining the target point based on the at least one aberrant ROI comprises:

determining whether the at least one abnormal ROI is located in the regulatable brain region.

If yes, determining the center of the at least one abnormal ROI as a target point, or determining a region which takes the center of the at least one abnormal ROI as a sphere center and a preset target point radius as a first target point ROI, and determining the target point according to the position of the first target point ROI.

If not, determining the connectivity of the at least one abnormal ROI and other ROIs in the at least two ROIs, and determining the ROIs which are positioned in the adjustable area and have the connectivity with the at least one abnormal ROI exceeding a preset connectivity threshold value in the other ROIs as second target spot candidate areas;

and determining the center of the second target point candidate region as a target point, or determining a region with the center of the second target point candidate region as a sphere center and a preset target point radius as a second target point ROI, and determining the target point according to the position of the second target point ROI.

In some alternative embodiments, determining a target point based on at least one aberrant ROI comprises:

determining at least one abnormal ROI or the intersection of the ROI with the abnormal ROI connectivity meeting a preset connectivity threshold condition and a brain structure partition as a target candidate area;

and determining the center of the target spot candidate region as the target spot, or determining the region with the center of the target spot candidate region as the sphere center and the preset target spot radius as the target spot ROI, and determining the target spot according to the position of the target spot ROI.

The disease type includes a disease type determined by a diagnosis of the subject or a disease type corresponding to a symptom to be treated in the subject.

The brain region corresponding relation of the disease type can be inquired according to the existing brain region corresponding relation of the determined disease type, and can also be set according to actual requirements. The manner in which the brain region for the type of disease is obtained is by way of example only and is not particularly limited.

Here, the target brain region is a brain region corresponding to the target, and the target brain region have a neural relationship, so that the target brain region can be subjected to neural regulation through stimulation on the target.

The target point may include coordinates corresponding to a single voxel, or may be a set of regions formed by some voxels.

In some optional embodiments, the step 204 may specifically include:

and determining the central position of at least one target brain area as a target.

In some optional embodiments, the step 204 may specifically include:

and determining the central position of at least one target point brain area as a sphere center, a region with a preset target point radius as a target point ROI, and determining the position of the target point ROI as a target point.

The length of the preset target point radius is not specifically limited, and the preset target point radius can be set according to the actual requirement of neural regulation, for example, the preset target point radius can be 3mm.

In some optional embodiments, the step 204 may specifically include:

determining the brain structure partition where the target point is located according to the disease type, determining the intersection of at least one target brain area and the brain structure partition, and determining the target point in the intersection.

In some alternative embodiments, the target needs to satisfy the following conditions: the target point can not be located on the inner side surface and the bottom of the brain, the target point can be located in the gyrus and the target point can not be located in the sulcus.

The target point determined according to the method is accurate, in practical application, scientific research personnel or medical care personnel can perform nerve regulation and control navigation on a subject according to the target point determined by the method by using optical navigation equipment or electromagnetic navigation equipment, and the effective rate of nerve regulation and control can be improved.

The present disclosure provides a neuromodulation apparatus configured for neuromodulating a target of a subject according to a predetermined neuromodulation protocol, wherein the target of the subject is determined according to the target determination method of any of the above embodiments of the present disclosure.

The neuromodulation devices may include implantable neuromodulation devices and non-implantable neuromodulation devices, such as: an event-related potential analysis system, an electroencephalogram system, a brain-computer interface device, and the like. The present disclosure is not intended to be limited to the particular forms of neuromodulation devices, which are presented herein by way of example only.

The target point of the subject is regulated and controlled, the operator can regulate and control the neural regulation and control equipment after connecting according to the target point, or the neural regulation and control equipment can regulate and control the target point of the subject according to the input of the operator or the active acquisition of the neural regulation and control equipment. This is merely an example and is not a specific limitation on the neuromodulation of a subject's target, as the skilled artisan may operate in accordance with the actual neuromodulation device usage. Illustratively, the preset neuromodulation protocol may include, but is not limited to:

a. electrical pulse sequence based neuromodulation protocols

i. Deep brain electrical stimulation

Transcranial electrical stimulation

Electric seizure related therapy

Electrical stimulation based on cortical brain electrodes

v. related derivatives of the above techniques

b. Magnetic pulse sequence based neuromodulation scheme

i. Transcranial magnetic stimulation and related protocols

Related derivatives of the above

c. Ultrasound-based neuromodulation protocols

i. Ultrasound focused neuromodulation scheme

Magnetic resonance guided high-energy ultrasound focused therapy system and related regulation and control scheme

Related derivatives of the above

d. Light-based neuromodulation protocols

i. Different band light stimulation and related schemes

Related derivatives of the above

With the gradual development of novel nerve regulation equipment and nerve regulation technology, the target point determination method disclosed by the invention can be used for determining the target point of nerve regulation in future nerve regulation equipment and nerve regulation schemes, and the method also belongs to the protection scope of the disclosure.

According to the method for establishing the accurate individual brain atlas, the functional information of each part of the brain can be efficiently and reliably acquired, and the accuracy of brain area positioning is improved. The functional positioning is carried out by means of the brain atlas at the accurate individual level, and the reliability of the positioning result of the nerve regulation target point is improved.

With further reference to fig. 5, as an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a target point determination apparatus, which corresponds to the method embodiment shown in fig. 2, and which can be applied in various electronic devices.

As shown in fig. 5, the target point determination device 500 of the present embodiment includes: a data acquisition unit 501, a processing unit 502, an abnormality detection unit 503, and a target point determination unit 504. Wherein:

a data obtaining unit 501 configured to obtain scan data of a subject, where the scan data includes data obtained by performing magnetic resonance imaging on a brain of the subject, and the scan data includes a blood oxygen level dependent BOLD signal sequence corresponding to each voxel in a preset number of voxels; a processing unit 502 configured to determine at least two regions of interest, ROIs, of the subject based on the scan data; an abnormality detection unit 503 configured to determine at least one abnormal ROI among the at least two ROIs according to a preset abnormality detection rule; a target point determination unit 504 configured to determine a target point based on the at least one abnormal ROI.

In some optional embodiments, the processing unit 502 is further configured to:

acquiring a BOLD signal sequence corresponding to each voxel in scanning data;

at least two ROIs are formed based on a brain region template of a standard brain and a subject brain connection matrix.

In some optional embodiments, the processing unit 502 is further configured to:

acquiring a BOLD signal sequence corresponding to each voxel in scanning data;

determining the connectivity between every two voxels in the scanning data based on the BOLD signal sequence corresponding to each voxel;

dividing the anatomical structure of the brain of the subject corresponding to the scan data into a plurality of large regions, and dissecting the plurality of large regions into a plurality of brain regions, wherein each brain region in the plurality of brain regions comprises at least one voxel;

and fusing brain areas of which the voxel connection degrees between the brain areas are higher than a preset brain area voxel connection degree threshold value in the plurality of brain areas to form at least two ROI.

In some optional embodiments, the processing unit 502 is further configured to:

at least two ROIs of the subject are determined from the scan data based on a volumetric standard brain structure template.

In some optional embodiments, the processing unit 502 is further configured to:

at least two ROIs of the subject are determined from the scan data based on a cortical standard brain structure template.

In some optional embodiments, the anomaly detection unit 503 is further configured to:

acquiring group brain magnetic resonance data;

acquiring a BOLD signal sequence corresponding to each voxel in scanning data;

In some alternative embodiments, the target point determination unit 504 is further configured to:

if so, determining the center of the at least one abnormal ROI as the target point, or determining a region with the center of the at least one abnormal ROI as a sphere center and a preset target point radius as a first target point ROI, and determining the target point according to the position of the first target point ROI.

If not, determining the connectivity of the at least one abnormal ROI and other ROIs in the at least two ROIs, and determining the ROIs which are positioned in the adjustable and controllable area and have the connectivity with the at least one abnormal ROI exceeding a preset connectivity threshold value in the other ROIs as second target point candidate areas;

In some alternative embodiments, magnetic resonance imaging, comprises: structural magnetic resonance imaging, and/or task state functional magnetic resonance imaging, and/or rest state functional magnetic resonance imaging.

It should be noted that, the details of implementation and technical effects of the units in the target point determination device provided by the present disclosure may refer to other embodiments in the present disclosure, and are not described herein again.

Referring now to FIG. 6, there is illustrated a block diagram of a computer system 600 suitable for use in implementing the terminal devices or servers of the present disclosure. The terminal device or server shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the present disclosure.

As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU) 601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data necessary for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM603 are connected to each other via a bus 604. An Input/Output (I/O) interface 605 is also connected to bus 604.

The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, mouse, etc. Including an output portion 607 such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker. A storage section 608 including a hard disk and the like. And a communication section 609 including a Network interface card such as a LAN (Local Area Network) card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet.

In particular, the processes described above with reference to the flow diagrams may be implemented as computer software programs, according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication section 609. The above-described functions defined in the method of the present disclosure are performed when the computer program is executed by a Central Processing Unit (CPU) 601. It should be noted that the computer readable medium of the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer-readable signal medium may include a propagated data signal with computer-readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + +, python, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in this disclosure may be implemented by software or hardware. The described units may also be provided in a processor, which may be described as: a processor includes a scan data acquisition unit, a setting unit, a processing unit, and a target point determination unit. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.

As another aspect, the present disclosure also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments. Or may be present separately and not assembled into the device. The computer readable medium carries one or more programs which, when executed by the apparatus, cause the apparatus to: acquiring scanning data of a subject, wherein the scanning data comprises data obtained by performing magnetic resonance imaging on the brain of the subject, and the scanning data comprises a blood oxygen level dependent BOLD signal sequence corresponding to each voxel in a preset number of voxels; determining at least two ROI of interest of the subject based on the scan data; determining at least one abnormal ROI in the at least two ROIs according to a preset abnormal detection rule; a target point is identified based on the at least one aberrant ROI.

The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is possible without departing from the inventive concept as defined above. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.

The technical solutions described in the embodiments of the present disclosure can be arbitrarily combined without conflict.

The above is only a specific embodiment of the present disclosure, but the scope of the present disclosure is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present disclosure, and shall be covered by the scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.

Claims

1. A target determination method, comprising:

acquiring scan data of a subject, wherein the scan data comprises data obtained by magnetic resonance imaging of the brain of the subject;

determining at least two ROI of interest of the subject based on the scan data;

determining at least one abnormal ROI in the at least two ROIs according to a preset abnormal detection rule;

identifying a target point based on the at least one aberrant ROI;

said determining at least two regions of interest, ROIs, of the subject based on the scan data, comprising in particular:

determining a connectivity between every two voxels in the scan data;

and fusing the brain areas with the voxel connectivity between the brain areas higher than a preset brain area voxel connectivity threshold value in the plurality of brain areas to form at least two ROI (regions of interest).

2. The method of claim 1, wherein said determining at least one abnormal ROI among said at least two ROIs according to preset abnormal detection rules comprises:

acquiring population brain magnetic resonance data;

3. The method of claim 1, wherein said determining a target point based on said at least one abnormal ROI comprises:

4. The method of claim 1, wherein said determining a target point based on said at least one abnormal ROI comprises:

and determining the center of the target point candidate region as the target point, or determining a region with the center of the target point candidate region as a sphere center and a preset target point radius as the target point ROI, and determining the target point according to the position of the target point ROI.

5. The method of claim 1, wherein the magnetic resonance imaging comprises: brain structure magnetic resonance imaging, and/or task state functional magnetic resonance imaging, and/or resting state functional magnetic resonance imaging.

6. A target determination device, comprising:

a data acquisition unit configured to acquire scan data of a subject, wherein the scan data comprises data resulting from magnetic resonance imaging of the brain of the subject;

a processing unit configured to determine at least two ROI of interest of the subject based on the scan data;

an abnormality detection unit configured to determine at least one abnormal ROI among the at least two ROIs according to a preset abnormality detection rule;

a target determination unit configured to determine a target based on the at least one abnormal ROI;

said determining at least two regions of interest, ROIs, of said subject based on said scan data, comprising in particular:

determining a connectivity between every two voxels in the scan data;

7. An electronic device, comprising:

one or more processors;

storage having one or more programs stored thereon that, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-5.

8. A computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by one or more processors, implements the method of any one of claims 1-5.

9. A neuromodulation device configured to neuromodulate a target of a subject in accordance with a preset neuromodulation protocol; wherein the target is determined according to the method of any one of claims 1-5.

10. The apparatus of claim 9, wherein the preset regulatory scheme comprises at least one of:

deep brain electrical stimulation;

transcranial electrical stimulation;

electrical seizure therapy;

electrical stimulation based on cortical brain electrodes;

transcranial magnetic stimulation;

ultrasound focusing neural regulation;

and (4) regulating and controlling light stimulation.